The embeddings \(\widehat{\mathbf {u}}_1, \ldots \widehat{\mathbf {u}}_r\)  and their associated scores \(\widehat{\lambda }_1, \ldots , \widehat{\lambda }_r\)  returned by the PCAN algorithm have three straightforward interpretations, which provide an easy to understand description of the network sample \(\mathbf {G}_N\) . We state these properties without proof as they are well-understood (see Chapter 10 of [1]} for example).
